Why is SMB important?
Surface mass balance can be thought of as a balance of the gain and loss on the surface of the Antarctic Ice Sheet. Precipitation serves to increase the SMB and surface erosion, meltwater runoff and sublimation decrease it (Kittel et al., 2021; Kromer and Trusel, 2023).
Understanding how SMB and ice shelf discharge are changing is important for understanding how the Antarctic Ice Sheet will contribute to future changes in sea level (Kittel et al., 2021).
This dataset documents the evolution of the Surface Mass Balance (SMB) of the Antarctic Ice Sheet (grounded and ice shelves included). Time series of annual and seasonal sums are available for the period 1979-present are documented. The estimates are computed by the regional climate model MAR, version 3.12 (see: Agosta et al., 2019 ; Kittel et al., 2021 for the description of the model and configuration of the model to the Antarctic Ice Sheet) forced by the ERA5 reanalysis (Hersbach et al., 2020). SMB is integrated over each MAR pixel whose ice fraction is higher than 30 %; i.e., over a 14 005 568 km² area. An evaluation of the model can be found in Kittel et al. (2021). The dataset is continuously updated depending on the availability of the reanalysis, and will also be updated when a new version of MAR is released.
This dataset documents the evolution of the Surface Mass Balance (SMB) of the Antarctic Ice Sheet (grounded and ice shelves included). The time series of annual and seasonal sums are available for the period 1979-present. We have used the regional climate model HIRHAM5 (Christensen et al., 2007) to compute the atmospheric variables, HIRHAM5 calculates the full surface energy balance at the surface and the amount of snowfall, rainfall, water vapour deposition and snow sublimation that occurs at the surface. It is downscaled to 0.11◦ (≈ 12.5 km) spatial resolution. The outputs from HIRHAM5 are then used as input in the offline SMB model (Langen et al., 2017). For the full Antarctic set-up, see Hansen et al., 2021. HIRHAM5 has been forced with the reanalysis dataset ERA5 (Hersbach et al., 2020).
C. Kittel (Institut des Géosciences de l’Environnement (IGE), Univ. Grenoble Alpes/CNRS/IRD/G-INP, Grenoble, France – [email protected]), C. Agosta (Laboratoire des Sciences du Climat et de l'Environnement, LSCE-IPSL, CEA-CNRS-UVSQ, Université Paris-Saclay, Gif-sur-Yvette, France), C. Amory (Institut des Géosciences de l’Environnement (IGE), Univ. Grenoble Alpes/CNRS/IRD/G-INP, Grenoble, France ).
Nicolaj Hansen ([email protected]), National Centre for Climate Research, Danish Meteorological Institute (DMI), Copenhagen, Denmark. The Danish state has supported the development of this dataset through the National Centre for Climate Research (NCKF), which has also provided computational resources. Furthermore, we would like to thank The European Centre for Medium-Range Weather Forecasting for providing the ERA5 dataset.
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